Temperature Variation
Water temperatures in Lake Champlain reached a minimum in February.
Sampling for this project began during the Spring warming period.
Temperature variability (both ranges and variance) increase with
temperature, but are strongly affected by the length of time period
examined.
# Lake Champlain near Burlington, VT
siteNumber = "04294500"
ChamplainInfo = readNWISsite(siteNumber)
parameterCd = "00010"
startDate = "2023-01-01"
endDate = ""
#statCd = c("00001", "00002","00003", "00011") # 1 - max, 2 - min, 3 = mean
# Constructs the URL for the data wanted then downloads the data
url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd,
startDate = startDate, endDate = endDate, service = "uv")
temp_data = importWaterML1(url, asDateTime = T) %>%
mutate("date" = as.Date(dateTime)) %>%
select(date, "temp" = X_00010_00000)
## Daily values
daily_temp_data = temp_data %>%
ungroup() %>%
group_by(date) %>%
summarise(mean_temp = mean(temp),
med_temp = median(temp),
var_temp = var(temp),
min_temp = min(temp),
max_temp = max(temp)) %>%
mutate("range_temp" = max_temp - min_temp)
day_prior_temp_data = temp_data %>%
ungroup() %>%
group_by(date) %>%
summarise(mean_temp = mean(temp),
med_temp = median(temp),
var_temp = var(temp),
min_temp = min(temp),
max_temp = max(temp)) %>%
mutate(date = date + 1) %>%
rename_with(.fn = ~ paste0("prior_day_", .x), .cols = c(-date))
daily_plot = daily_temp_data %>%
pivot_longer(cols = c(-date),
names_to = "parameter",
values_to = "temp") %>%
ggplot(aes(x = date, y = temp, colour = parameter)) +
geom_line(linewidth = 1) +
scale_colour_manual(values = c(
"mean_temp" = "olivedrab3",
"med_temp" = "seagreen3",
"max_temp" = "tomato",
"min_temp" = "dodgerblue",
"range_temp" = "goldenrod3",
"var_temp" = "darkgoldenrod1"
)) +
scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) +
ggtitle("Daily Values") +
labs(y = "Temperature (°C)",
x = "") +
theme_bw(base_size = 20) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
## Defining the function to get predictor values for periods of different lengths
get_predictors = function(daily_values, raw_temp, n_days){
prefix = str_replace_all(xfun::numbers_to_words(n_days), pattern = " ", replacement = "-")
mean_values = daily_values %>%
ungroup() %>%
mutate(mean_max = slide_vec(.x = max_temp, .f = mean, .before = n_days, .complete = T),
mean_min = slide_vec(.x = min_temp, .f = mean, .before = n_days, .complete = T),
mean_range = slide_vec(.x = range_temp, .f = mean, .before = n_days, .complete = T)) %>%
select(date, mean_max, mean_min, mean_range) %>%
rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))
period_values = raw_temp %>%
mutate(mean = slide_index_mean(temp, i = date, before = days(n_days),
na_rm = T),
max = slide_index_max(temp, i = date, before = days(n_days),
na_rm = T),
min = slide_index_min(temp, i = date, before = days(n_days),
na_rm = T),
med = slide_index_dbl(temp, .i = date, .before = days(n_days),
na_rm = T, .f = median),
var = slide_index_dbl(temp, .i = date, .before = days(n_days),
.f = var),
range = max - min) %>%
select(-temp) %>%
distinct() %>%
rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))%>%
inner_join(mean_values, by = c("date")) %>%
drop_na()
return(period_values)
}
## Getting predictor variables for different periods
### Short (three days)
three_day_temps = get_predictors(daily_values = daily_temp_data,
raw_temp = temp_data,
n_days = 3)
### ONE WEEK
week_temps = get_predictors(daily_values = daily_temp_data,
raw_temp = temp_data,
n_days = 7)
week_plot = week_temps %>%
pivot_longer(cols = c(-date),
names_to = "parameter",
values_to = "temp") %>%
filter(parameter %in% c("seven_day_mean",
"seven_day_med",
"seven_day_max",
"seven_day_min",
"seven_day_var",
"seven_day_range")) %>%
mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>%
ggplot(aes(x = date, y = temp, colour = parameter)) +
geom_line(linewidth = 1) +
scale_colour_manual(values = c(
"mean_temp" = "olivedrab3",
"med_temp" = "seagreen3",
"max_temp" = "tomato",
"min_temp" = "dodgerblue",
"range_temp" = "goldenrod3",
"var_temp" = "darkgoldenrod1"
)) +
scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) +
ggtitle("One Week") +
labs(y = "Temperature (°C)",
x = "") +
theme_bw(base_size = 20) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
### TWO WEEKS
two_week_temps = get_predictors(daily_values = daily_temp_data,
raw_temp = temp_data,
n_days = 14)
two_week_plot = two_week_temps %>%
pivot_longer(cols = c(-date),
names_to = "parameter",
values_to = "temp") %>%
filter(parameter %in% c("fourteen_day_mean",
"fourteen_day_med",
"fourteen_day_max",
"fourteen_day_min",
"fourteen_day_var",
"fourteen_day_range")) %>%
mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>%
ggplot(aes(x = date, y = temp, colour = parameter)) +
geom_line(linewidth = 1) +
scale_colour_manual(values = c(
"mean_temp" = "olivedrab3",
"med_temp" = "seagreen3",
"max_temp" = "tomato",
"min_temp" = "dodgerblue",
"range_temp" = "goldenrod3",
"var_temp" = "darkgoldenrod1"
)) +
scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) +
ggtitle("Two Weeks") +
labs(y = "Temperature (°C)",
x = "") +
theme_bw(base_size = 20) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
### FOUR WEEKS
four_week_temps = get_predictors(daily_values = daily_temp_data,
raw_temp = temp_data,
n_days = 28)
four_week_plot = four_week_temps %>%
pivot_longer(cols = c(-date),
names_to = "parameter",
values_to = "temp") %>%
filter(parameter %in% c("twenty-eight_day_mean",
"twenty-eight_day_med",
"twenty-eight_day_max",
"twenty-eight_day_min",
"twenty-eight_day_var",
"twenty-eight_day_range")) %>%
mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>%
ggplot(aes(x = date, y = temp, colour = parameter)) +
geom_line(linewidth = 1) +
scale_colour_manual(values = c(
"mean_temp" = "olivedrab3",
"med_temp" = "seagreen3",
"max_temp" = "tomato",
"min_temp" = "dodgerblue",
"range_temp" = "goldenrod3",
"var_temp" = "darkgoldenrod1"
)) +
scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) +
ggtitle("Four Weeks") +
labs(y = "Temperature (°C)",
x = "") +
theme_bw(base_size = 20) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
### EIGHT WEEKS
eight_week_temps = get_predictors(daily_values = daily_temp_data,
raw_temp = temp_data,
n_days = 56)
eight_week_plot = eight_week_temps %>%
pivot_longer(cols = c(-date),
names_to = "parameter",
values_to = "temp") %>%
filter(parameter %in% c("fifty-six_day_mean",
"fifty-six_day_med",
"fifty-six_day_max",
"fifty-six_day_min",
"fifty-six_day_var",
"fifty-six_day_range")) %>%
mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>%
ggplot(aes(x = date, y = temp, colour = parameter)) +
geom_line(linewidth = 1) +
scale_colour_manual(values = c(
"mean_temp" = "olivedrab3",
"med_temp" = "seagreen3",
"max_temp" = "tomato",
"min_temp" = "dodgerblue",
"range_temp" = "goldenrod3",
"var_temp" = "darkgoldenrod1"
)) +
scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) +
ggtitle("Eight Weeks") +
labs(y = "Temperature (°C)",
x = "") +
theme_bw(base_size = 20) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
ggarrange(daily_plot, week_plot, two_week_plot, four_week_plot, eight_week_plot,
common.legend = T, nrow = 1, legend = "bottom")

one_week_doy_data = week_temps %>%
mutate(doy = yday(date))
one_week_temp_circle = ggplot(one_week_doy_data, aes(x = seven_day_mean_max, y = seven_day_mean_min, colour = doy)) +
geom_point() +
scale_colour_gradient(
high = "coral2",
low = "dodgerblue4") +
labs(x = "Max. Temp. (°C)",
y = "Min. Temp. (°C)") +
labs(x = "Max. Temp. (°C)",
y = "Min. Temp. (°C)") +
ggtitle("One Week") +
theme_matt()
The different time periods examined by this climate data highlights
that the relationship between minimum and maximum temperatures changes
based on the window examined. For example, minimum and maximum
temperatures experienced over weekly intervals are closely linked,
whereas there is a distinct seasonal cycle in the relationship between
minimum and maximum temperatures experienced over periods of four
weeks.
four_week_doy_data = four_week_temps %>%
mutate(doy = yday(date))
four_week_temp_circle = ggplot(four_week_doy_data, aes(x = `twenty-eight_day_max`, y = `twenty-eight_day_min`, colour = doy)) +
geom_point() +
scale_colour_gradient(
high = "coral2",
low = "dodgerblue4") +
labs(x = "Max. Temp. (°C)",
y = "Min. Temp. (°C)") +
ggtitle("Four Week") +
theme_matt()
ggarrange(one_week_temp_circle, four_week_temp_circle,
common.legend = T, legend = "bottom")

## Daily values for the period examined by dataset
collection_conditions = temp_data %>%
ungroup() %>%
group_by(date) %>%
summarise(mean_temp = mean(temp),
med_temp = median(temp),
var_temp = var(temp),
min_temp = min(temp),
max_temp = max(temp)) %>%
mutate("range_temp" = max_temp - min_temp,
date = as.Date(date)) %>%
ungroup() %>%
filter(date >= (min(as.Date(full_data$collection_date)) - 7))
## Mean female thermal limits for each species, grouped by collection
species_summaries = full_data %>%
#filter(sex == "female") %>%
group_by(sp_name, collection_date, collection_temp) %>%
summarise("mean_ctmax" = mean(ctmax),
"sample_size" = n(),
"ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
"ctmax_var" = var(ctmax),
"mean_size" = mean(size),
"size_st_err" = (sd(size) / sqrt(sample_size)),
"size_var" = var(size)) %>%
ungroup() %>%
complete(sp_name, collection_date) %>%
arrange(desc(sample_size))
adult_summaries = full_data %>%
filter(sex == "female") %>%
group_by(sp_name, collection_date, collection_temp) %>%
summarise("mean_ctmax" = mean(ctmax),
"sample_size" = n(),
"ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
"ctmax_var" = var(ctmax),
"mean_size" = mean(size),
"size_st_err" = (sd(size) / sqrt(sample_size)),
"size_var" = var(size)) %>%
ungroup() %>%
complete(sp_name, collection_date) %>%
arrange(desc(sample_size))
ggplot() +
geom_vline(data = unique(select(full_data, collection_date)),
aes(xintercept = as.Date(collection_date)),
colour = "grey90",
linewidth = 1) +
geom_line(data = collection_conditions,
aes(x = as.Date(date), y = mean_temp),
colour = "black",
linewidth = 2) +
# geom_errorbar(data = species_summaries,
# aes(x = as.Date(collection_date),
# ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
# colour = sp_name),
# position = position_dodge(width = 1),
# width = 5, linewidth = 1) +
geom_point(data = species_summaries,
aes(x = as.Date(collection_date), y = mean_ctmax, colour = sp_name, size = sample_size)) +
scale_colour_manual(values = species_cols) +
labs(x = "Date",
y = "Temperature (°C)",
colour = "Species",
size = "Sample Size") +
theme_matt() +
theme(legend.position = "right")

size_timeseries = ggplot() +
geom_vline(data = unique(select(full_data, collection_date)),
aes(xintercept = as.Date(collection_date)),
colour = "grey90",
linewidth = 1) +
geom_line(data = collection_conditions,
aes(x = as.Date(date), y = mean_temp),
colour = "black",
linewidth = 2) +
# geom_errorbar(data = species_summaries,
# aes(x = as.Date(collection_date),
# ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
# colour = sp_name),
# position = position_dodge(width = 1),
# width = 5, linewidth = 1) +
geom_point(data = species_summaries,
aes(x = as.Date(collection_date), y = mean_size * 40, colour = sp_name),
position = position_dodge(width = 1),
size = 4) +
scale_colour_manual(values = species_cols) +
scale_y_continuous(
name = "Temperature", # Features of the first axis
sec.axis = sec_axis(~./40, name="Prosome Length (mm)"), # Add a second axis and specify its features
breaks = c(0,5,10,15,20,25,30)
) +
labs(x = "Date",
y = "Temperature (°C)",
colour = "Species") +
theme_matt() +
theme(legend.position = "right")
#ggarrange(ctmax_timeseries, size_timeseries, common.legend = T, legend = "bottom")
## Combine data, then pull out values for each collection date
date_list = as.Date(unique(full_data$collection_date))
temp_predictors = daily_temp_data %>%
full_join(day_prior_temp_data, by = c("date")) %>%
full_join(three_day_temps, by = c("date")) %>%
full_join(week_temps, by = c("date")) %>%
full_join(two_week_temps, by = c("date")) %>%
full_join(four_week_temps, by = c("date")) %>%
full_join(eight_week_temps, by = c("date")) %>%
filter(date %in% date_list)
A set of predictors variables were assembled from the continuous
temperature data set based on conditions during the day of collection,
the week before collections, and the preceding two, four, and eight week
periods. This is a preliminary analysis for now. Shown here are the top
three factors. Species with no significant predictor or limited
collection date distributions were excluded.
num_colls = full_data %>%
filter(sex == "female") %>%
select(collection_date, sp_name) %>%
distinct() %>%
count(sp_name) %>%
filter(n >= 5)
corr_vals = full_data %>%
filter(sp_name %in% num_colls$sp_name) %>%
filter(sex == "female") %>%
mutate(collection_date = as.Date(collection_date)) %>%
full_join(temp_predictors, join_by(collection_date == date)) %>%
pivot_longer(cols = c(collection_temp, mean_temp:tail(names(.), 1)),
values_to = "value",
names_to = "predictor") %>%
group_by(sp_name, predictor) %>%
summarise(correlation = cor.test(ctmax, value)$estimate,
p.value = cor.test(ctmax, value)$p.value,
ci_low = cor.test(ctmax, value)$conf.int[1],
ci_high = cor.test(ctmax, value)$conf.int[2]) %>%
mutate(sig = ifelse(p.value <0.05, "Sig.", "Non Sig."))
corr_vals %>%
filter(sig == "Sig.") %>%
drop_na(correlation) %>%
group_by(sp_name) %>%
arrange(desc(correlation)) %>%
slice_head(n = 3) %>%
select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value) %>%
knitr::kable(align = "c")
| Epischura lacustris |
three_day_mean_min |
0.7776747 |
0.0136206 |
| Epischura lacustris |
prior_day_min_temp |
0.7697148 |
0.0152761 |
| Epischura lacustris |
three_day_med |
0.7693684 |
0.0153510 |
| Leptodiaptomus minutus |
three_day_mean_max |
0.6074635 |
0.0000000 |
| Leptodiaptomus minutus |
three_day_max |
0.6047785 |
0.0000000 |
| Leptodiaptomus minutus |
prior_day_max_temp |
0.6039838 |
0.0000000 |
| Skistodiaptomus oregonensis |
prior_day_max_temp |
0.5111761 |
0.0000000 |
| Skistodiaptomus oregonensis |
three_day_mean_max |
0.5034440 |
0.0000000 |
| Skistodiaptomus oregonensis |
three_day_max |
0.4951207 |
0.0000000 |
Trait Variation
ctmax_plot = full_data %>%
mutate( #sp_name = str_replace(sp_name, pattern = " ",
# replacement = "\n"),
sp_name = fct_reorder(sp_name, ctmax, mean)) %>%
ggplot(aes(y = sp_name, x = ctmax)) +
geom_point(aes(colour= sp_name_sub),
position = position_dodge(width = 0.3),
size = 4) +
scale_colour_manual(values = species_cols) +
xlab(NULL) +
labs(y = "",
x = "CTmax (°C)",
colour = "Group") +
theme_matt() +
theme(legend.position = "none")
size_plot = full_data %>%
mutate(sp_name = fct_reorder(sp_name, ctmax, mean)) %>%
ggplot(aes(y = sp_name, x = size)) +
geom_point(aes(colour= sp_name_sub),
position = position_dodge(width = 0.3),
size = 4) +
scale_colour_manual(values = species_cols) +
labs(x = "Prosome Length (mm)",
y = "",
colour = "Group") +
guides(color = guide_legend(ncol = 1)) +
theme_matt(base_size = ) +
theme(legend.position = "right",
axis.text.y = element_blank(),
plot.margin = margin(0, 0, 0, 0,"cm"))
trait_plot = ctmax_plot + size_plot
trait_plot

full_data %>%
drop_na(fecundity) %>%
ggplot(aes(x = fecundity, fill = sp_name_sub)) +
facet_wrap(.~sp_name_sub, ncol = 1) +
geom_histogram(binwidth = 2) +
scale_fill_manual(values = species_cols) +
labs(x = "Fecundity (# Eggs)") +
theme_matt_facets() +
theme(legend.position = "none")

Variation with temperature
ctmax_temp = ggplot(full_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) +
geom_smooth(method = "lm", linewidth = 3) +
geom_point(size = 3) +
labs(x = "Collection Temperature (°C)",
y = "CTmax (°C)",
colour = "Species") +
scale_colour_manual(values = species_cols) +
theme_matt() +
theme(legend.position = "right")
size_temp = ggplot(filter(full_data, sex != "juvenile"), aes(x = collection_temp, y = size, colour = sp_name)) +
geom_smooth(method = "lm", linewidth = 3) +
geom_point(size = 3) +
labs(x = "Collection Temperature (°C)",
y = "Length (mm)",
colour = "Species") +
scale_colour_manual(values = species_cols) +
theme_matt() +
theme(legend.position = "right")
wt_temp = ggplot(full_data, aes(x = collection_temp, y = warming_tol, colour = sp_name)) +
geom_smooth(method = "lm", linewidth = 3) +
geom_point(size = 3) +
labs(x = "Collection Temperature (°C)",
y = "Warming Tolerance (°C)",
colour = "Species") +
scale_colour_manual(values = species_cols) +
theme_matt() +
theme(legend.position = "right")
eggs_temp = ggplot(full_data, aes(x = collection_temp, y = fecundity, colour = sp_name)) +
geom_smooth(method = "lm", linewidth = 3) +
geom_point(size = 3) +
labs(x = "Collection Temperature (°C)",
y = "Fecundity (# Eggs)",
colour = "Species") +
scale_colour_manual(values = species_cols) +
theme_matt() +
theme(legend.position = "right")
ggarrange(ctmax_temp, size_temp, wt_temp, eggs_temp,
common.legend = T, legend = "right")

# adult_data = full_data %>%
# filter(sex == "female")
model_data = full_data %>%
drop_na(size, ctmax)
ctmax_temp.model = lm(data = model_data, ctmax ~ collection_temp * sp_name + sex)
size_temp.model = lm(data = model_data, size ~ collection_temp * sp_name + sex)
knitr::kable(car::Anova(ctmax_temp.model))
| collection_temp |
255.243899 |
1 |
196.8817833 |
0.0000000 |
| sp_name |
1820.429275 |
6 |
234.0306422 |
0.0000000 |
| sex |
6.318581 |
2 |
2.4369113 |
0.0887084 |
| collection_temp:sp_name |
2.368759 |
3 |
0.6090455 |
0.6094493 |
| Residuals |
526.351506 |
406 |
NA |
NA |
ctmax_resids = cbind(model_data, "resids" = ctmax_temp.model$residuals, "size_resids" = size_temp.model$residuals)
ggplot(ctmax_resids, aes(x = days_in_lab, y = resids, colour = sp_name)) +
facet_wrap(sp_name~.) +
geom_point(size = 4, alpha = 0.5) +
geom_smooth(method = "lm", se = F, linewidth = 2) +
scale_x_continuous(breaks = c(0:5)) +
labs(x = "Days in lab",
y = "CTmax Residuals") +
scale_colour_manual(values = species_cols) +
theme_matt_facets() +
theme(legend.position = "none")

Given the long generation times of these copepods, decreases in trait
variance may indicate selection over the seasonal cycle. Shown below are
the variance in observed CTmax and size, plotted against collection
date. Variance decreases in Skistodiaptomus, but this pattern
is driven by a single collection with high variance early in the year.
Size variance increases slightly in Skistodiaptomus. Variance
in both CTmax and size is fairly constant in Leptodiaptomus
minutus, the only other species collected across the entire set of
samples thus far.
ggplot(drop_na(adult_summaries, ctmax_var), aes(x = as.Date(collection_date), y = ctmax_var, colour = sp_name)) +
facet_wrap(sp_name~., scales = "free_y") +
geom_point(size = 2) +
geom_smooth(method = "lm", se = F) +
labs(x = "Collection Temp. (°C)",
y = "CTmax Variance") +
scale_colour_manual(values = species_cols) +
theme_matt_facets() +
theme(legend.position = "none")

ggplot(drop_na(adult_summaries, size_var), aes(x = as.Date(collection_date), y = size_var, colour = sp_name)) +
facet_wrap(sp_name~.) +
geom_point(size = 2) +
geom_smooth(method = "lm", se = F) +
labs(x = "Collection Temp. (°C)",
y = "Size Variance") +
scale_colour_manual(values = species_cols) +
theme_matt_facets() +
theme(legend.position = "none")

Predicting Vulnerability
Using the observed thermal limit data, we can produce a hindcast of
thermal stress for Lake Champlain copepods. For these initial assays, we
will define thermal stress as any time when maximum daily water
temperature is within 2°C of copepod CTmax or higher. We will use three
different scenarios: 1) the average CTmax for each species, 2) CTmax
predicted using collection temperatures, and 3) for species that have
sufficient data, CTmax predicted using whichever environmental factor is
the strongest candidate for driving acclimation. In all cases, data is
filtered to just thermal limits of adult females.
Scenario 1
mean_ctmax = full_data %>%
filter(sex == "female") %>%
group_by(sp_name) %>%
summarize("mean_ctmax" = mean(ctmax)) %>%
arrange(mean_ctmax)
knitr::kable(mean_ctmax)
| Senecella calanoides |
23.90509 |
| Limnocalanus macrurus |
25.30001 |
| Leptodiaptomus sicilis |
30.96424 |
| Leptodiaptomus minutus |
32.83846 |
| Epischura lacustris |
34.15190 |
| Pseudodiaptomus sp |
36.31250 |
| Skistodiaptomus oregonensis |
37.02116 |
# # Constructs the URL for the full temperature data set; RUN THIS ONCE
# hind_url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd, service = "uv")
#
# hind_temp_data = importWaterML1(hind_url, asDateTime = T) %>%
# mutate("date" = as.Date(dateTime)) %>%
# select(date, "temp" = X_00010_00000)
#
# write.table(x = hind_temp_data, file = "hindcast_temps.csv", row.names = F, sep = ",")
# ggplot(hind_temp_data, aes(x = date, y = temp)) +
# geom_line(linewidth = 0.1) +
# labs(x = "Date",
# y = "Water Temperature (°C)") +
# theme_matt()
In the simplest scenario, species thermal limits are static through
time, represented by the average CTmax of adult female copepods. In this
scenario, only three of the seven observed species are exposed to
thermal stress (temperatures within 5°C of CTmax). Temperatures
approached the thermal limit of Leptodiaptomus sicilis on a
handful of days. By contrast, Senecella calanoides and
Limnocalanus macrurus were both exposed to substantial thermal
stress throughout a large portion of the year, likely explaining why
these species are absent from the community for the summer and fall
periods.
hind1_data = hind_temp_data %>%
group_by(date) %>%
summarize("daily_max" = max(temp),
"daily_mean" = mean(temp),) %>%
bind_cols(pivot_wider(mean_ctmax, names_from = sp_name, values_from = mean_ctmax)) %>%
pivot_longer(cols = c(-date, -daily_max, -daily_mean),
names_to = "species",
values_to = "mean_ctmax") %>%
mutate(lim_diff = mean_ctmax - daily_max) %>%
mutate(doy = yday(date),
"method" = "No_acclimation")
hind_daily_temp_data = hind_temp_data %>%
ungroup() %>%
group_by(date) %>%
summarise(mean_temp = mean(temp),
med_temp = median(temp),
var_temp = var(temp),
min_temp = min(temp),
max_temp = max(temp)) %>%
mutate("range_temp" = max_temp - min_temp)
#table(hind1_data$species)
hind1_data %>%
filter(lim_diff <= 5) %>%
ggplot(aes(x = doy, y = lim_diff, colour = species)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 5,
colour = "grey") +
geom_point(alpha = 0.5) +
geom_smooth() +
labs(x = "Day of Year",
y = "Predicted Warming Tolerance \n(°C Above Daily Max)") +
theme_matt() +
theme(legend.position = "right")

Scenario 2
In the second scenario, thermal limits vary within and between
species. A simple model is used to predict species thermal limits based
on mean daily temperature (CTmax as a function of species and collection
temperature, but without the interaction between these two factors).
These predicted thermal limits are then compared against the maximum
daily temperature to estimate thermal stress, as in Scenario 1.
Including this simple form of acclimation in the model reduced the
degree of thermal stress for each species, eliminating it entirely for
Leptodiaptomus sicilis. Note that the magnitude of the
predicted stress is low enough that removing the 5°C buffer around the
predicted thermal limits would actually limit predicted thermal stress
to just a few days for Senecella calanoides.
hindcast_model1 = lm(data = filter(full_data, sex == "female"),
ctmax ~ collection_temp + sp_name)
hind2_data = hind_temp_data %>%
group_by(date) %>%
summarize("collection_temp" = mean(temp),
"daily_max" = max(temp)) %>%
bind_cols(
pivot_wider(mean_ctmax,
names_from = sp_name,
values_from = mean_ctmax)) %>%
pivot_longer(cols = c(-date, -daily_max, -collection_temp),
names_to = "sp_name",
values_to = "mean_ctmax") %>%
select(-mean_ctmax) %>%
mutate("pred_ctmax" = predict.lm (hindcast_model1, newdata = .)) %>%
select(date, "daily_mean" = collection_temp, daily_max, "species" = sp_name, pred_ctmax) %>%
mutate(lim_diff = pred_ctmax - daily_max) %>%
#filter(lim_diff <= 0) %>%
mutate(doy = yday(date),
"method" = "Constant_acclimation")
# ggplot(hind2_data, aes(x = daily_mean, y = pred_ctmax, colour = species)) +
# geom_smooth(method = "lm")
# table(hind2_data$species)
hind2_data %>%
filter(lim_diff <= 5) %>%
ggplot(aes(x = doy, y = lim_diff, colour = species)) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 5,
colour = "grey") +
geom_point(alpha = 0.5) +
geom_smooth() +
labs(x = "Day of Year",
y = "Predicted Warming Tolerance \n(°C Above Daily Max)") +
theme_matt() +
theme(legend.position = "right")

Scenario 3
The final scenario allows the environmental variable used to predict
CTmax to vary between species. For species observed in fewer than 5
collections, we use the same approach as in Scenario 2. For species
observed in more than 5 collections, however, the factor with the
strongest correlation with CTmax is used to predict thermal limits.
These factors are included below.
hind_preds = corr_vals %>%
filter(sig == "Sig.") %>%
drop_na(correlation) %>%
group_by(sp_name) %>%
arrange(desc(correlation)) %>%
slice_head(n = 1) %>%
select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value)
knitr::kable(hind_preds, align = "c")
| Epischura lacustris |
three_day_mean_min |
0.7776747 |
0.0136206 |
| Leptodiaptomus minutus |
three_day_mean_max |
0.6074635 |
0.0000000 |
| Skistodiaptomus oregonensis |
prior_day_max_temp |
0.5111761 |
0.0000000 |
hind3_data = hind2_data %>% # Contains data for species that won't change from scenario 2
filter(!(species %in% corr_vals$sp_name))
words_to_numbers <- function(s) {
s <- stringr::str_to_lower(s)
for (i in 0:56)
s <- stringr::str_replace_all(s, words(i), as.character(i))
s
}
preds_to_pull = hind_preds %>%
select(Species, Predictor) %>%
mutate(n_days = str_split_fixed(Predictor, pattern = "_", n = 2)[,1],
parameter = str_split_fixed(Predictor, pattern = "_day_", n = 2)[,2])
for(i in 1:length(preds_to_pull$Species)){
if(preds_to_pull$n_days[i] == "prior"){ #The prior day temperature metrics should be used
duration = NA
predictors = hind_daily_temp_data %>%
mutate(date = date + 1)
parameter = str_split_fixed(preds_to_pull$parameter[i], pattern = "_temp", n = 2)[1]
model_data = full_data %>%
filter(sp_name %in% preds_to_pull$Species[i]) %>%
filter(sex == "female") %>%
mutate(collection_date = as_date(collection_date)) %>%
inner_join(predictors, join_by(collection_date == date)) %>%
select(ctmax, contains(parameter))
if(dim(model_data)[2] == 2){
hind.model = lm(data = model_data,
ctmax ~ .)
sp_data = predictors %>%
select(date, contains(parameter)) %>%
mutate(pred_ctmax = predict(hind.model, newdata = .)) %>%
select(date, pred_ctmax) %>%
inner_join(hind_daily_temp_data, by = c("date")) %>%
mutate("species" = preds_to_pull$Species[i],
"doy" = yday(date),
lim_diff = pred_ctmax - max_temp) %>%
select(date, daily_mean = mean_temp, daily_max = max_temp, species, pred_ctmax, lim_diff, doy)
hind3_data = bind_rows(hind3_data, sp_data)
}else{
print("Too many columns selected")
}
}else{
duration = as.numeric(words_to_numbers(preds_to_pull$n_days[i]))
}
if(preds_to_pull$n_days[i] != "prior" & is.na(duration)){ #Daily temperatures should be used, as in Scenario 2
sp_data = hind2_data %>%
filter(species == preds_to_pull$Species[i])
hind3_data = bind_rows(hind3_data, sp_data)
}
if(is.numeric(duration)){
#Neither the prior day nor day of metrics should be used; use duration as n_days
predictors = get_predictors(daily_values = hind_daily_temp_data,
raw_temp = hind_temp_data,
n_days = duration)
parameter = str_split_fixed(preds_to_pull$parameter[i], pattern = "_temp", n = 2)[1]
model_data = full_data %>%
filter(sp_name %in% preds_to_pull$Species[i]) %>%
filter(sex == "female") %>%
mutate(collection_date = as_date(collection_date)) %>%
inner_join(predictors, join_by(collection_date == date)) %>%
select(ctmax, contains(parameter))
if(dim(model_data)[2] == 2){
hind.model = lm(data = model_data,
ctmax ~ .)
sp_data = predictors %>%
select(date, contains(parameter)) %>%
mutate(pred_ctmax = predict(hind.model, newdata = .)) %>%
select(date, pred_ctmax) %>%
inner_join(hind_daily_temp_data, by = c("date")) %>%
mutate("species" = preds_to_pull$Species[i],
"doy" = yday(date),
lim_diff = pred_ctmax - max_temp) %>%
select(date, daily_mean = mean_temp, daily_max = max_temp, species, pred_ctmax, lim_diff, doy)
hind3_data = bind_rows(hind3_data, sp_data)
}else{
print("Too many columns selected")
}
}
}
hind3_data = hind3_data %>%
mutate("method" = "Variable_acclimation")
This third approach did not affect the predicted patterns in
Limnocalanus or Senecella (neither species has been
observed in enough collections to estimate the effects of different
environmental factors). Changing the acclimation approach did affect
patterns in thermal limits in the other species though. The figure below
shows how predicted warming tolerance varies over the year in the seven
species, based on the three different prediction methods. In general,
constant thermal limits (the ‘no acclimation’ method) resulted in larger
warming tolerance during the winter and lower warming tolerance during
the summer, although this effect was small in most species.
synthesis = bind_rows(
select(hind1_data, date, doy, daily_mean, daily_max, species, "pred_ctmax" = mean_ctmax, lim_diff, method),
select(hind2_data, date, doy, daily_mean, daily_max, species, pred_ctmax, lim_diff, method),
select(hind3_data, date, doy, daily_mean, daily_max, species, pred_ctmax, lim_diff, method)) %>%
mutate(method = fct_relevel(method, "No_acclimation", "Constant_acclimation", "Variable_acclimation"))
climatology = synthesis %>%
group_by(species, doy, method) %>%
summarise("mean_diff" = mean(lim_diff),
"min_diff" = min(lim_diff),
"max_diff" = max(lim_diff)) %>%
mutate(method = fct_relevel(method, "No_acclimation", "Constant_acclimation", "Variable_acclimation"))
acc_effects = synthesis %>%
pivot_wider(id_cols = c(date, species, doy),
names_from = method,
values_from = lim_diff) %>%
mutate("const_acc_effect" = Constant_acclimation - No_acclimation,
"var_acc_effect" = Variable_acclimation - No_acclimation)
ggplot(synthesis, aes(x = doy, y = lim_diff, colour = method)) +
facet_wrap(species~.) +
geom_hline(yintercept = 0) +
geom_hline(yintercept = 5, colour = "grey") +
geom_point(alpha = 0.1) +
labs(x = "Day of Year",
y = "Predicted Warming Tolerance (°C Above Daily Max)") +
theme_matt_facets(base_size = 18) +
theme(strip.text.x.top = element_text(size = 10))

---
title: Seasonality in Lake Champlain Copepod Thermal Limits
date: "`r Sys.Date()`"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          html_preview: false
          toc: true
          toc_depth: 3
---

```{r to-do}
### To Do 

# Actual statistics for relationships between temperature and CTmax, size, and fecundity
# Pull residuals from CTmax ~ temperature model, and examine the change over time in lab and the relationship with fecundity

```


```{r setup, include=T, message = F, warning = F, echo = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "center",
  fig.path = "../Figures/markdown/",
  dev = c("png", "pdf"),
  message = FALSE,
  warning = FALSE,
  collapse = T
)

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

theme_matt_facets = function(base_size = 18,
                             dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  theme_bw(base_family="sans") %+replace% 
    theme(
      panel.grid = element_blank(),
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      strip.text.x = element_text(size = base_size),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title.x = element_text(size = base_size * 1.2,
                                  margin = unit(c(3, 0, 0, 0), "mm")),
      axis.title.y = element_text(size = base_size * 1.2,
                                  margin = unit(c(0, 5, 0, 0), "mm"), 
                                  angle = 90),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

species_cols = c("Leptodiaptomus minutus" = "#ffd029",
                 "Leptodiaptomus minutus juvenile" = "#e3d8af",
                 "Leptodiaptomus minutus male" = "#ffe896",
                 "Leptodiaptomus sicilis" = "#D86F29",
                 "Leptodiaptomus sicilis male" = "#E28C00",
                 "Skistodiaptomus oregonensis" = "#C5C35A",
                 "Skistodiaptomus oregonensis male" = "#e6e6aa", 
                 "Epischura lacustris juvenile" = "plum1", 
                 "Epischura lacustris male" = "plum3", 
                 "Epischura lacustris" = "plum4", 
                 "Limnocalanus macrurus" = "skyblue4", 
                 "Limnocalanus macrurus male" = "skyblue3", 
                 "Limnocalanus macrurus juvenile" = "skyblue", 
                 "Senecella calanoides" = "darkseagreen3",
                 "Leptodora kindti male" = "lightblue3",
                 "Leptodora kindti" = "lightblue4",
                 "Leptodora kindti juvenile" = "lightblue",
                 "Pseudodiaptomus sp" = "lightcoral")
```

## Copepod Collection

Copepods were collected at approximately weekly intervals from Lake Champlain (Burlington Fishing Pier). Plankton was collected from the top 3 meters using a 250 um mesh net. Copepods from `r length(unique(full_data$collection_date))` collections were used to make a total of `r dim(full_data)[1]` thermal limit measurements. Over this time period, collection temperatures ranged from `r paste(min(full_data$collection_temp), " to ", max(full_data$collection_temp), sep = "")`°C. 

## Temperature Variation 
Water temperatures in Lake Champlain reached a minimum in February. Sampling for this project began during the Spring warming period. Temperature variability (both ranges and variance) increase with temperature, but are strongly affected by the length of time period examined.    

```{r pulling-temp-data}
# Lake Champlain near Burlington, VT
siteNumber = "04294500"
ChamplainInfo = readNWISsite(siteNumber)
parameterCd = "00010"
startDate = "2023-01-01"
endDate = ""
#statCd = c("00001", "00002","00003", "00011") # 1 - max, 2 - min, 3 = mean

# Constructs the URL for the data wanted then downloads the data
url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd, 
                       startDate = startDate, endDate = endDate, service = "uv")

temp_data = importWaterML1(url, asDateTime = T) %>% 
  mutate("date" = as.Date(dateTime)) %>% 
  select(date, "temp" = X_00010_00000)


## Daily values
daily_temp_data = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp)

day_prior_temp_data = temp_data %>% 
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate(date = date + 1) %>% 
  rename_with(.fn = ~ paste0("prior_day_", .x), .cols = c(-date))

daily_plot = daily_temp_data %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Daily Values") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))
```

```{r predictors-function}
## Defining the function to get predictor values for periods of different lengths
get_predictors = function(daily_values, raw_temp, n_days){
  prefix = str_replace_all(xfun::numbers_to_words(n_days), pattern = " ", replacement = "-")
  
  mean_values = daily_values %>% 
    ungroup() %>% 
    mutate(mean_max = slide_vec(.x = max_temp, .f = mean, .before = n_days, .complete = T),
           mean_min = slide_vec(.x = min_temp, .f = mean, .before = n_days, .complete = T),
           mean_range = slide_vec(.x = range_temp, .f = mean, .before = n_days, .complete = T)) %>% 
    select(date, mean_max, mean_min, mean_range) %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))
  
  period_values = raw_temp %>% 
    mutate(mean = slide_index_mean(temp, i = date, before = days(n_days), 
                                   na_rm = T),
           max = slide_index_max(temp, i = date, before = days(n_days), 
                                 na_rm = T),
           min = slide_index_min(temp, i = date, before = days(n_days),
                                 na_rm = T),
           med = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 na_rm = T, .f = median),
           var = slide_index_dbl(temp, .i = date, .before = days(n_days), 
                                 .f = var),
           range = max - min) %>%  
    select(-temp) %>%  
    distinct() %>% 
    rename_with( ~ paste(prefix, "day", .x, sep = "_"), .cols = c(-date))%>% 
    inner_join(mean_values, by = c("date")) %>%  
    drop_na()
  
  return(period_values)
}
```


```{r predictors-and-plots, fig.width=12, fig.height=5}
## Getting predictor variables for different periods

### Short (three days)
three_day_temps = get_predictors(daily_values = daily_temp_data, 
                                 raw_temp = temp_data, 
                                 n_days = 3)

### ONE WEEK
week_temps = get_predictors(daily_values = daily_temp_data, 
                            raw_temp = temp_data, 
                            n_days = 7)

week_plot = week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("seven_day_mean",
                          "seven_day_med",
                          "seven_day_max", 
                          "seven_day_min", 
                          "seven_day_var",
                          "seven_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("One Week") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### TWO WEEKS
two_week_temps = get_predictors(daily_values = daily_temp_data, 
                                raw_temp = temp_data, 
                                n_days = 14)

two_week_plot = two_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fourteen_day_mean",
                          "fourteen_day_med",
                          "fourteen_day_max", 
                          "fourteen_day_min", 
                          "fourteen_day_var",
                          "fourteen_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Two Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### FOUR WEEKS
four_week_temps = get_predictors(daily_values = daily_temp_data, 
                                 raw_temp = temp_data, 
                                 n_days = 28)

four_week_plot = four_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("twenty-eight_day_mean",
                          "twenty-eight_day_med",
                          "twenty-eight_day_max", 
                          "twenty-eight_day_min", 
                          "twenty-eight_day_var",
                          "twenty-eight_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Four Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))


### EIGHT WEEKS
eight_week_temps = get_predictors(daily_values = daily_temp_data, 
                                  raw_temp = temp_data, 
                                  n_days = 56)

eight_week_plot = eight_week_temps %>% 
  pivot_longer(cols = c(-date),
               names_to = "parameter", 
               values_to = "temp") %>% 
  filter(parameter %in% c("fifty-six_day_mean",
                          "fifty-six_day_med",
                          "fifty-six_day_max", 
                          "fifty-six_day_min", 
                          "fifty-six_day_var",
                          "fifty-six_day_range")) %>% 
  mutate(parameter = paste(word(parameter, start = 3, sep = fixed("_")), "_temp", sep = "")) %>% 
  ggplot(aes(x = date, y = temp, colour = parameter)) + 
  geom_line(linewidth = 1) + 
  scale_colour_manual(values = c(
    "mean_temp" = "olivedrab3",
    "med_temp" = "seagreen3",
    "max_temp" = "tomato",  
    "min_temp" = "dodgerblue",
    "range_temp" = "goldenrod3",
    "var_temp" = "darkgoldenrod1"
  )) + 
  scale_x_continuous(breaks = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01"))) + 
  ggtitle("Eight Weeks") + 
  labs(y = "Temperature (°C)",
       x = "") + 
  theme_bw(base_size = 20) + 
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 270, hjust = 0, vjust = 0.5))

ggarrange(daily_plot, week_plot, two_week_plot, four_week_plot, eight_week_plot, 
          common.legend = T, nrow = 1, legend = "bottom")
```

```{r}
one_week_doy_data = week_temps %>% 
  mutate(doy = yday(date))

one_week_temp_circle = ggplot(one_week_doy_data, aes(x = seven_day_mean_max, y = seven_day_mean_min, colour = doy)) + 
  geom_point() + 
  scale_colour_gradient(
    high = "coral2",
    low = "dodgerblue4") + 
  labs(x = "Max. Temp. (°C)",
       y = "Min. Temp. (°C)") + 
  labs(x = "Max. Temp. (°C)",
       y = "Min. Temp. (°C)") + 
  ggtitle("One Week") + 
  theme_matt()
```

The different time periods examined by this climate data highlights that the relationship between minimum and maximum temperatures changes based on the window examined. For example, minimum and maximum temperatures experienced over weekly intervals are closely linked, whereas there is a distinct seasonal cycle in the relationship between minimum and maximum temperatures experienced over periods of four weeks. 

```{r}
four_week_doy_data = four_week_temps %>% 
  mutate(doy = yday(date))

four_week_temp_circle = ggplot(four_week_doy_data, aes(x = `twenty-eight_day_max`, y = `twenty-eight_day_min`, colour = doy)) + 
  geom_point() + 
  scale_colour_gradient(
    high = "coral2",
    low = "dodgerblue4") + 
  labs(x = "Max. Temp. (°C)",
       y = "Min. Temp. (°C)") + 
  ggtitle("Four Week") + 
  theme_matt()

ggarrange(one_week_temp_circle, four_week_temp_circle,
          common.legend = T, legend = "bottom")
```


```{r ctmax-timeseries, fig.width=10, fig.height=5}
## Daily values for the period examined by dataset
collection_conditions = temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp,
         date = as.Date(date)) %>% 
  ungroup() %>%  
  filter(date >= (min(as.Date(full_data$collection_date)) - 7))

## Mean female thermal limits for each species, grouped by collection
species_summaries = full_data %>%  
  #filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))

adult_summaries = full_data %>%  
  filter(sex == "female") %>% 
  group_by(sp_name, collection_date, collection_temp) %>%  
  summarise("mean_ctmax" = mean(ctmax),
            "sample_size" = n(),
            "ctmax_st_err" = (sd(ctmax) / sqrt(sample_size)),
            "ctmax_var" = var(ctmax), 
            "mean_size" = mean(size),
            "size_st_err" = (sd(size) / sqrt(sample_size)),
            "size_var" = var(size)) %>%  
  ungroup() %>% 
  complete(sp_name, collection_date) %>% 
  arrange(desc(sample_size))

ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date),
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) +
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_ctmax, colour = sp_name, size = sample_size)) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species",
       size = "Sample Size") + 
  theme_matt() + 
  theme(legend.position = "right")

size_timeseries = ggplot() + 
  geom_vline(data = unique(select(full_data, collection_date)), 
             aes(xintercept = as.Date(collection_date)),
             colour = "grey90",
             linewidth = 1) + 
  geom_line(data = collection_conditions, 
            aes(x = as.Date(date), y = mean_temp),
            colour = "black", 
            linewidth = 2) + 
  # geom_errorbar(data = species_summaries,
  #               aes(x = as.Date(collection_date), 
  #                   ymin = mean_ctmax - ctmax_st_err, ymax = mean_ctmax + ctmax_st_err,
  #                   colour = sp_name),
  #               position = position_dodge(width = 1),
  #               width = 5, linewidth = 1) + 
  geom_point(data = species_summaries, 
             aes(x = as.Date(collection_date), y = mean_size * 40, colour = sp_name),
             position = position_dodge(width = 1),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  scale_y_continuous(
    name = "Temperature", # Features of the first axis
    sec.axis = sec_axis(~./40, name="Prosome Length (mm)"), # Add a second axis and specify its features
    breaks = c(0,5,10,15,20,25,30)
  ) + 
  labs(x = "Date", 
       y = "Temperature (°C)", 
       colour = "Species") + 
  theme_matt() + 
  theme(legend.position = "right")

#ggarrange(ctmax_timeseries, size_timeseries, common.legend = T, legend = "bottom")
```


```{r pulling-predictors}
## Combine data, then pull out values for each collection date
date_list = as.Date(unique(full_data$collection_date))

temp_predictors = daily_temp_data %>% 
  full_join(day_prior_temp_data, by = c("date")) %>% 
  full_join(three_day_temps, by = c("date")) %>% 
  full_join(week_temps, by = c("date")) %>% 
  full_join(two_week_temps, by = c("date")) %>% 
  full_join(four_week_temps, by = c("date")) %>% 
  full_join(eight_week_temps, by = c("date")) %>% 
  filter(date %in% date_list)
```

A set of predictors variables were assembled from the continuous temperature data set based on conditions during the day of collection, the week before collections, and the preceding two, four, and eight week periods. This is a preliminary analysis for now. Shown here are the top three factors. Species with no significant predictor or limited collection date distributions were excluded. 

```{r predictor-correlations}
num_colls = full_data %>% 
  filter(sex == "female") %>% 
  select(collection_date, sp_name) %>%  
  distinct() %>%  
  count(sp_name) %>% 
  filter(n >= 5)

corr_vals = full_data %>%
  filter(sp_name %in% num_colls$sp_name) %>% 
  filter(sex == "female") %>% 
  mutate(collection_date = as.Date(collection_date)) %>% 
  full_join(temp_predictors, join_by(collection_date == date)) %>% 
  pivot_longer(cols = c(collection_temp, mean_temp:tail(names(.), 1)),
               values_to = "value", 
               names_to = "predictor") %>%  
  group_by(sp_name, predictor) %>% 
  summarise(correlation = cor.test(ctmax, value)$estimate,
            p.value = cor.test(ctmax, value)$p.value,
            ci_low = cor.test(ctmax, value)$conf.int[1],
            ci_high = cor.test(ctmax, value)$conf.int[2]) %>% 
  mutate(sig = ifelse(p.value <0.05, "Sig.", "Non Sig."))

corr_vals %>%  
  filter(sig == "Sig.") %>% 
  drop_na(correlation) %>% 
  group_by(sp_name) %>%
  arrange(desc(correlation)) %>% 
  slice_head(n = 3) %>% 
  select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value) %>% 
  knitr::kable(align = "c")
```

## Trait Variation 
```{r ctmax-and-size-sum-plot, fig.width=20, fig.height=5}
ctmax_plot = full_data %>% 
  mutate( #sp_name = str_replace(sp_name, pattern = " ",
    #                              replacement = "\n"),
    sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = ctmax)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  xlab(NULL) + 
  labs(y = "",
       x = "CTmax (°C)",
       colour = "Group") + 
  theme_matt() + 
  theme(legend.position = "none")

size_plot = full_data %>% 
  mutate(sp_name = fct_reorder(sp_name, ctmax, mean)) %>% 
  ggplot(aes(y = sp_name, x = size)) + 
  geom_point(aes(colour= sp_name_sub),
             position = position_dodge(width = 0.3),
             size = 4) + 
  scale_colour_manual(values = species_cols) + 
  labs(x = "Prosome Length (mm)",
       y = "", 
       colour = "Group") + 
  guides(color = guide_legend(ncol = 1)) +
  theme_matt(base_size = ) + 
  theme(legend.position = "right",
        axis.text.y = element_blank(),
        plot.margin = margin(0, 0, 0, 0,"cm"))

trait_plot = ctmax_plot + size_plot
trait_plot
```

```{r ctmax-and-size-histograms, fig.width=7, fig.height=21, include = F}
ggplot(full_data, aes(x = size, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 0.05) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Prosome length (mm)") + 
  theme_matt_facets() + 
  theme(legend.position = "none")

ggplot(full_data, aes(x = ctmax, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 1) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "CTmax (°C)") + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```

```{r fecundity-histogram, fig.width=7, fig.height=10}
full_data %>%  
  drop_na(fecundity) %>%  
  ggplot(aes(x = fecundity, fill = sp_name_sub)) + 
  facet_wrap(.~sp_name_sub, ncol = 1) + 
  geom_histogram(binwidth = 2) + 
  scale_fill_manual(values = species_cols) + 
  labs(x = "Fecundity (# Eggs)") +
  theme_matt_facets() + 
  theme(legend.position = "none")
```

### Variation with temperature 

```{r trait-coll-temp-plots, fig.width=15, fig.height=10}
ctmax_temp = ggplot(full_data, aes(x = collection_temp, y = ctmax, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

size_temp = ggplot(filter(full_data, sex != "juvenile"), aes(x = collection_temp, y = size, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Length (mm)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

wt_temp = ggplot(full_data, aes(x = collection_temp, y = warming_tol, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Warming Tolerance (°C)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

eggs_temp = ggplot(full_data, aes(x = collection_temp, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", linewidth = 3) +
  geom_point(size = 3) + 
  labs(x = "Collection Temperature (°C)", 
       y = "Fecundity (# Eggs)",
       colour = "Species")  + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")

ggarrange(ctmax_temp, size_temp, wt_temp, eggs_temp, 
          common.legend = T, legend = "right")
```

```{r ctmax-coll-temp-model}
# adult_data = full_data %>% 
#   filter(sex == "female")
model_data = full_data %>%  
  drop_na(size, ctmax)

ctmax_temp.model = lm(data = model_data, ctmax ~ collection_temp * sp_name + sex)
size_temp.model = lm(data = model_data, size ~ collection_temp * sp_name + sex)

knitr::kable(car::Anova(ctmax_temp.model))

ctmax_resids = cbind(model_data, "resids" = ctmax_temp.model$residuals, "size_resids" = size_temp.model$residuals)
```

```{r ctmax-time-in-lab, fig.width=15, fig.height=10}
ggplot(ctmax_resids, aes(x = days_in_lab, y = resids, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 4, alpha = 0.5) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  scale_x_continuous(breaks = c(0:5)) + 
  labs(x = "Days in lab", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```

Given the long generation times of these copepods, decreases in trait variance may indicate selection over the seasonal cycle. Shown below are the variance in observed CTmax and size, plotted against collection date. Variance decreases in *Skistodiaptomus*, but this pattern is driven by a single collection with high variance early in the year. Size variance increases slightly in *Skistodiaptomus*. Variance in both CTmax and size is fairly constant in *Leptodiaptomus minutus*, the only other species collected across the entire set of samples thus far. 

```{r trait-variance-coll-temp}
ggplot(drop_na(adult_summaries, ctmax_var), aes(x = as.Date(collection_date), y = ctmax_var, colour = sp_name)) + 
  facet_wrap(sp_name~., scales = "free_y") + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "CTmax Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")

ggplot(drop_na(adult_summaries, size_var), aes(x = as.Date(collection_date), y = size_var, colour = sp_name)) + 
  facet_wrap(sp_name~.) + 
  geom_point(size = 2) + 
  geom_smooth(method = "lm", se = F) + 
  labs(x = "Collection Temp. (°C)", 
       y = "Size Variance") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt_facets() + 
  theme(legend.position = "none")
```


## Sex and stage variation in thermal limits 
Previous sections have generally lumped juvenile, female, and male individuals together. There may be important stage- or sex-specific differences in CTmax though. For several species, we have measurements for individuals in different stages or of different sexes. 

```{r sex-stage-table}
sex_sample_sizes = ctmax_resids %>%  
  group_by(sp_name, sex) %>%  
  summarise(num = n()) %>%  
  pivot_wider(id_cols = sp_name,
              names_from = sex, 
              values_from = num,
              values_fill = 0) %>% 
  select("Species" = sp_name, "Juvenile" = juvenile, "Female" = female, "Male" = male)

knitr::kable(sex_sample_sizes, align = "c")
```

The female-male and female-juvenile comparisons show that there are generally no differences in thermal limits between these groups. 

```{r ctmax-sex, fig.width=7, fig.height=7}
ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Male > 0, Female > 0)$Species & 
           sex != "juvenile") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 2) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax Residuals") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())
```

```{r ctmax-stage, fig.width=3, fig.height=6}
ctmax_resids %>% 
  filter(sp_name %in% filter(sex_sample_sizes, Juvenile > 0 & Female > 0)$Species & 
           sex != "male") %>% 
  ggplot(aes(x = sex, y = resids, colour = sp_name, group = sp_name)) + 
  facet_wrap(sp_name~., ncol = 1) + 
  geom_smooth(method = "lm", se = F, linewidth = 1) + 
  geom_point(size = 3,
             alpha = 0.5,
             position = position_jitter(height = 0, width = 0.05)) +  
  labs(x = "Sex", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_bw(base_size = 18) + 
  theme(legend.position = "none", 
        panel.grid = element_blank())
```

## Trait Correlations 

```{r ctmax-size, fig.width=10, fig.height=7}

full_data %>% 
  #filter(sex == "female") %>%  
  ggplot( aes(x = size, y = ctmax, colour = sp_name)) + 
  geom_smooth(data = full_data, 
              aes(x = size, y = ctmax),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_point(size = 2, alpha = 0.4) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r ctmaxresids-size, fig.width=10, fig.height=7, include = F}
ctmax_resids %>% 
  filter(sex == "female") %>% 
  ggplot(aes(x = size_resids, y = resids, colour = sp_name)) + 
  geom_smooth(data = ctmax_resids, 
              aes(x = size_resids, y = resids),
              method = "lm", 
              colour ="black", 
              linewidth = 2.5) + 
  geom_point(size = 2, alpha = 0.4) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  labs(x = "Length (mm)", 
       y = "CTmax (°C)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```


```{r fecundity-size, fig.width=10, fig.height=7}
ggplot(ctmax_resids, aes(x = size, y = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 2, alpha = 0.5) + 
  labs(x = "Prosome length (mm)", 
       y = "Fecundity (# Eggs)",
       colour = "Species") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r, ctmax-fecundity, fig.width=10, fig.height=7}
ggplot(ctmax_resids, aes(y = resids, x = fecundity, colour = sp_name)) + 
  geom_smooth(method = "lm", se = F, linewidth = 2) + 
  geom_point(size = 2, alpha = 0.5) + 
  labs(y = "CTmax (°C)", 
       x = "Fecundity (# Eggs)") + 
  scale_colour_manual(values = species_cols) + 
  theme_matt() + 
  theme(legend.position = "right")
```

```{r}
if(predict_vuln == F){
  knitr::knit_exit()
}
```


## Predicting Vulnerability 
Using the observed thermal limit data, we can produce a hindcast of thermal stress for Lake Champlain copepods. For these initial assays, we will define thermal stress as any time when maximum daily water temperature is within 2°C of copepod CTmax or higher. We will use three different scenarios: 1) the average CTmax for each species, 2) CTmax predicted using collection temperatures, and 3) for species that have sufficient data, CTmax predicted using whichever environmental factor is the strongest candidate for driving acclimation. In all cases, data is filtered to just thermal limits of adult females. 

### Scenario 1
```{r}
mean_ctmax = full_data %>% 
  filter(sex == "female") %>%  
  group_by(sp_name) %>% 
  summarize("mean_ctmax" = mean(ctmax)) %>% 
  arrange(mean_ctmax)

knitr::kable(mean_ctmax)
```

```{r}
# # Constructs the URL for the full temperature data set; RUN THIS ONCE
# hind_url = constructNWISURL(siteNumbers = siteNumber, parameterCd = parameterCd, service = "uv")
# 
# hind_temp_data = importWaterML1(hind_url, asDateTime = T) %>%
#   mutate("date" = as.Date(dateTime)) %>%
#   select(date, "temp" = X_00010_00000)
# 
# write.table(x = hind_temp_data, file = "hindcast_temps.csv", row.names = F, sep = ",")
```

```{r}
# ggplot(hind_temp_data, aes(x = date, y = temp)) + 
#   geom_line(linewidth = 0.1) + 
#   labs(x = "Date", 
#        y = "Water Temperature (°C)") +
#   theme_matt()
```

In the simplest scenario, species thermal limits are static through time, represented by the average CTmax of adult female copepods. In this scenario, only three of the seven observed species are exposed to thermal stress (temperatures within 5°C of CTmax). Temperatures approached the thermal limit of *Leptodiaptomus sicilis* on a handful of days. By contrast, *Senecella calanoides* and *Limnocalanus macrurus* were both exposed to substantial thermal stress throughout a large portion of the year, likely explaining why these species are absent from the community for the summer and fall periods. 

```{r fig.width=10, fig.height=5}
hind1_data = hind_temp_data %>% 
  group_by(date) %>% 
  summarize("daily_max" = max(temp),
            "daily_mean" = mean(temp),) %>% 
  bind_cols(pivot_wider(mean_ctmax, names_from = sp_name, values_from = mean_ctmax)) %>%  
  pivot_longer(cols = c(-date, -daily_max, -daily_mean),
               names_to = "species", 
               values_to = "mean_ctmax") %>%  
  mutate(lim_diff = mean_ctmax - daily_max) %>%  
  mutate(doy = yday(date),
         "method" = "No_acclimation")

hind_daily_temp_data = hind_temp_data %>%
  ungroup() %>% 
  group_by(date) %>% 
  summarise(mean_temp = mean(temp),
            med_temp = median(temp),
            var_temp = var(temp), 
            min_temp = min(temp), 
            max_temp = max(temp)) %>% 
  mutate("range_temp" = max_temp - min_temp)

#table(hind1_data$species)

hind1_data %>% 
filter(lim_diff <= 5) %>%  
ggplot(aes(x = doy, y = lim_diff, colour = species)) +
  geom_hline(yintercept = 0) + 
    geom_hline(yintercept = 5, 
               colour = "grey") + 
  geom_point(alpha = 0.5) +
  geom_smooth() + 
  labs(x = "Day of Year", 
       y = "Predicted Warming Tolerance \n(°C Above Daily Max)") + 
  theme_matt() + 
  theme(legend.position = "right")
```

### Scenario 2
In the second scenario, thermal limits vary within and between species. A simple model is used to predict species thermal limits based on mean daily temperature (CTmax as a function of species and collection temperature, but without the interaction between these two factors). These predicted thermal limits are then compared against the maximum daily temperature to estimate thermal stress, as in Scenario 1. Including this simple form of acclimation in the model reduced the degree of thermal stress for each species, eliminating it entirely for *Leptodiaptomus sicilis*. Note that the magnitude of the predicted stress is  low enough that removing the 5°C buffer around the predicted thermal limits would actually limit predicted thermal stress to just a few days for *Senecella calanoides*. 

```{r fig.height=5, fig.width=10}
hindcast_model1 = lm(data = filter(full_data, sex == "female"),
                     ctmax ~ collection_temp + sp_name)

hind2_data = hind_temp_data %>% 
  group_by(date) %>% 
  summarize("collection_temp" = mean(temp),
            "daily_max" = max(temp)) %>% 
  bind_cols(
    pivot_wider(mean_ctmax, 
                names_from = sp_name, 
                values_from = mean_ctmax)) %>% 
  pivot_longer(cols = c(-date, -daily_max, -collection_temp),
               names_to = "sp_name", 
               values_to = "mean_ctmax") %>% 
  select(-mean_ctmax) %>% 
  mutate("pred_ctmax" = predict.lm (hindcast_model1, newdata = .)) %>% 
  select(date, "daily_mean" = collection_temp, daily_max, "species" = sp_name, pred_ctmax) %>% 
  mutate(lim_diff = pred_ctmax - daily_max) %>% 
  #filter(lim_diff <= 0) %>%  
  mutate(doy = yday(date),
         "method" = "Constant_acclimation")

# ggplot(hind2_data, aes(x = daily_mean, y = pred_ctmax, colour = species)) +
#   geom_smooth(method = "lm") 

# table(hind2_data$species)
hind2_data %>%  
  filter(lim_diff <= 5) %>%  
  ggplot(aes(x = doy, y = lim_diff, colour = species)) +
  geom_hline(yintercept = 0) + 
    geom_hline(yintercept = 5, 
               colour = "grey") + 
  geom_point(alpha = 0.5) +
  geom_smooth() + 
  labs(x = "Day of Year", 
       y = "Predicted Warming Tolerance \n(°C Above Daily Max)") + 
  theme_matt() + 
  theme(legend.position = "right")
```

### Scenario 3
The final scenario allows the environmental variable used to predict CTmax to vary between species. For species observed in fewer than 5 collections, we use the same approach as in Scenario 2. For species observed in more than 5 collections, however, the factor with the strongest correlation with CTmax is used to predict thermal limits. These factors are included below.

```{r}
hind_preds = corr_vals %>%  
  filter(sig == "Sig.") %>% 
  drop_na(correlation) %>% 
  group_by(sp_name) %>%
  arrange(desc(correlation)) %>% 
  slice_head(n = 1) %>% 
  select("Species" = sp_name, "Predictor" = predictor, "Correlation" = correlation, "P-Value" = p.value)

knitr::kable(hind_preds, align = "c")
```

```{r}
hind3_data = hind2_data %>% # Contains data for species that won't change from scenario 2
  filter(!(species %in% corr_vals$sp_name))

words_to_numbers <- function(s) {
  s <- stringr::str_to_lower(s)
  for (i in 0:56)
    s <- stringr::str_replace_all(s, words(i), as.character(i))
  s
}

preds_to_pull = hind_preds %>%  
  select(Species, Predictor) %>% 
  mutate(n_days = str_split_fixed(Predictor, pattern = "_", n = 2)[,1],
         parameter = str_split_fixed(Predictor, pattern = "_day_", n = 2)[,2])

for(i in 1:length(preds_to_pull$Species)){
  
  if(preds_to_pull$n_days[i] == "prior"){ #The prior day temperature metrics should be used
    duration = NA
    
    predictors = hind_daily_temp_data %>% 
      mutate(date = date + 1) 
    
    parameter = str_split_fixed(preds_to_pull$parameter[i], pattern = "_temp", n = 2)[1]
    
    model_data = full_data %>%
      filter(sp_name %in% preds_to_pull$Species[i]) %>% 
      filter(sex == "female") %>% 
      mutate(collection_date = as_date(collection_date)) %>% 
      inner_join(predictors, join_by(collection_date == date)) %>%  
      select(ctmax, contains(parameter))
    
    if(dim(model_data)[2] == 2){
      hind.model = lm(data = model_data, 
                      ctmax ~ .)
      
      sp_data = predictors %>% 
        select(date, contains(parameter)) %>% 
        mutate(pred_ctmax = predict(hind.model, newdata = .)) %>%  
        select(date, pred_ctmax) %>% 
        inner_join(hind_daily_temp_data, by = c("date")) %>% 
        mutate("species" = preds_to_pull$Species[i],
               "doy" = yday(date),
               lim_diff = pred_ctmax - max_temp) %>% 
        select(date, daily_mean = mean_temp, daily_max = max_temp, species, pred_ctmax, lim_diff, doy)
    
        hind3_data = bind_rows(hind3_data, sp_data)
    }else{
      print("Too many columns selected")
    }
    
    
  }else{
    duration = as.numeric(words_to_numbers(preds_to_pull$n_days[i]))
  }
  
  if(preds_to_pull$n_days[i] != "prior" & is.na(duration)){ #Daily temperatures should be used, as in Scenario 2
    sp_data = hind2_data %>% 
      filter(species == preds_to_pull$Species[i])
    
    hind3_data = bind_rows(hind3_data, sp_data)
  }
  
  if(is.numeric(duration)){
    #Neither the prior day nor day of metrics should be used; use duration as n_days
    
    predictors = get_predictors(daily_values = hind_daily_temp_data, 
                                raw_temp = hind_temp_data, 
                                n_days = duration)
    
    parameter = str_split_fixed(preds_to_pull$parameter[i], pattern = "_temp", n = 2)[1]
    
    model_data = full_data %>%
      filter(sp_name %in% preds_to_pull$Species[i]) %>% 
      filter(sex == "female") %>% 
      mutate(collection_date = as_date(collection_date)) %>% 
      inner_join(predictors, join_by(collection_date == date)) %>%  
      select(ctmax, contains(parameter))
    
    if(dim(model_data)[2] == 2){
      hind.model = lm(data = model_data, 
                      ctmax ~ .)
      
      sp_data = predictors %>% 
        select(date, contains(parameter)) %>% 
        mutate(pred_ctmax = predict(hind.model, newdata = .)) %>%  
        select(date, pred_ctmax) %>% 
        inner_join(hind_daily_temp_data, by = c("date")) %>% 
        mutate("species" = preds_to_pull$Species[i],
               "doy" = yday(date),
               lim_diff = pred_ctmax - max_temp) %>% 
        select(date, daily_mean = mean_temp, daily_max = max_temp, species, pred_ctmax, lim_diff, doy)
    
        hind3_data = bind_rows(hind3_data, sp_data)

    }else{
      print("Too many columns selected")
    }
    
  }
}

hind3_data = hind3_data %>% 
  mutate("method" = "Variable_acclimation")
```

This third approach did not affect the predicted patterns in *Limnocalanus* or *Senecella* (neither species has been observed in enough collections to estimate the effects of different environmental factors). Changing the acclimation approach did affect patterns in thermal limits in the other species though. The figure below shows how predicted warming tolerance varies over the year in the seven species, based on the three different prediction methods. In general, constant thermal limits (the 'no acclimation' method) resulted in larger warming tolerance during the winter and lower warming tolerance during the summer, although this effect was small in most species.     

```{r fig.width=10, fig.height=10}
synthesis = bind_rows(
  select(hind1_data, date, doy, daily_mean, daily_max, species, "pred_ctmax" = mean_ctmax, lim_diff, method),
  select(hind2_data, date, doy, daily_mean, daily_max,  species, pred_ctmax, lim_diff, method),
  select(hind3_data, date, doy, daily_mean, daily_max,  species, pred_ctmax, lim_diff, method)) %>% 
  mutate(method = fct_relevel(method, "No_acclimation", "Constant_acclimation", "Variable_acclimation"))

climatology = synthesis %>% 
  group_by(species, doy, method) %>%  
  summarise("mean_diff" = mean(lim_diff),
            "min_diff" = min(lim_diff),
            "max_diff" = max(lim_diff)) %>% 
  mutate(method = fct_relevel(method, "No_acclimation", "Constant_acclimation", "Variable_acclimation"))

acc_effects = synthesis %>% 
  pivot_wider(id_cols = c(date, species, doy), 
              names_from = method, 
              values_from = lim_diff) %>%  
  mutate("const_acc_effect" = Constant_acclimation - No_acclimation,
         "var_acc_effect" = Variable_acclimation - No_acclimation)

ggplot(synthesis, aes(x = doy, y = lim_diff, colour = method)) + 
  facet_wrap(species~.) + 
  geom_hline(yintercept = 0) + 
  geom_hline(yintercept = 5, colour = "grey") + 
  geom_point(alpha = 0.1) + 
  labs(x = "Day of Year", 
       y = "Predicted Warming Tolerance (°C Above Daily Max)") + 
  theme_matt_facets(base_size = 18) + 
  theme(strip.text.x.top = element_text(size = 10))
```
